Graph adjacency matrix approximation based recommendation system
ZHU Zhenfeng
GAO Hongge
ZHAO Yao
Abstract：In graph based recommendation methods,the goal of the graph constraint is to preserve the consistency of user relationships (item relationships) between high dimensional user representation space (item representation space) and low dimensional latent user representation space.Instead of applying the traditional Laplacian matrix based consistency constraint,a graph adjacency matrix approximation based recommendation model is proposed.In essence,the matrix approximation plays a role of directly imposing a consistency constraint on the different similarity metric spaces.Thus,not only the consistency of the user relationships (the item relationships) from different representation spaces can be well preserved,but also,the local over-fitting problem can be avoided to some extent.Experimental results on EachMovie and MovieLens datasets show the effectiveness of the proposed method.
Keywords：recommender systemcollaborative filteringmatrix factorizationgraph modelgradient descent method
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 1-7 )